基于迁移学习的航空发动机性能贝叶斯评估

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
P. Seshadri, A. Duncan, G. Thorne, G. Parks, Raul Vazquez Diaz, M. Girolami
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引用次数: 5

摘要

航空发动机性能由发动机内沿不同轴向位置的温度和压力分布决定。在传感器测量有限的情况下,我们需要一种统计原则性的方法来推断这些轮廓。在本文中,我们详细介绍了一种用于插值航空发动机轴向站的空间温度或压力分布的贝叶斯方法。任何给定轴向站的剖面都表示为环空上的空间高斯随机场,周向变化使用傅立叶基建模,径向变化使用平方指数核建模。该高斯随机场被扩展为从多个轴向测量平面摄取数据,目的是在平面之间传递信息。为了促进这种类型的迁移学习,提出了一种新的平面协方差核。在包括温度场的频率未知的情况下,我们利用频率上的稀疏性促进先验来鼓励稀疏表示。这很容易扩展到具有多个发动机平面的情况,同时适应平面之间的频率变化。感兴趣的主要量,空间面积平均值很容易以闭合形式获得。我们将其称为贝叶斯面积平均值,并展示了该度量如何提供比扇区面积平均值更具代表性的平均值——一种广泛使用的面积平均方法。此外,贝叶斯区域平均值自然地将后验不确定性分解为分别表征采样不足和传感器测量误差的项。这也比以前基于标准差的不确定性细分提供了显著的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian assessments of aeroengine performance with transfer learning
Aeroengine performance is determined by temperature and pressure profiles along various axial stations within an engine. Given limited sensor measurements, we require a statistically principled approach for inferring these profiles. In this paper we detail a Bayesian methodology for interpolating the spatial temperature or pressure profile at axial stations within an aeroengine. The profile at any given axial station is represented as a spatial Gaussian random field on an annulus, with circumferential variations modelled using a Fourier basis and radial variations modelled with a squared exponential kernel. This Gaussian random field is extended to ingest data from multiple axial measurement planes, with the aim of transferring information across the planes. To facilitate this type of transfer learning, a novel planar covariance kernel is proposed. In the scenario where frequencies comprising the temperature field are unknown, we utilise a sparsity-promoting prior on the frequencies to encourage sparse representations. This easily extends to cases with multiple engine planes whilst accommodating frequency variations between the planes. The main quantity of interest, the spatial area average is readily obtained in closed form. We term this the Bayesian area average and demonstrate how this metric offers far more representative averages than a sector area average---a widely used area averaging approach. Furthermore, the Bayesian area average naturally decomposes the posterior uncertainty into terms characterising insufficient sampling and sensor measurement error respectively. This too provides a significant improvement over prior standard deviation based uncertainty breakdowns.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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